Abstract
BackgroundIn the past ~15 years, the identification of diagnostic and prognostic biomarkers from gene expression data has increased our understanding of cancer biology and has led to advances in the personalized treatment of many cancers. A diagnostic biomarker is indicative of tumor status such as tumor stage, while a prognostic biomarker is indicative of disease outcome. Despite these advances, however, there are no clinically approved biomarkers for the treatment of bladder cancer, which is the fourth most common cancer in males in the United States and one of the most expensive cancers to treat. Although gene expression profiles of bladder cancer patients are publicly available, biomarker identification requires bioinformatics expertise that is not available to many research laboratories.DescriptionWe collected gene expression data from 13 publicly available patient cohorts (N = 1454) and developed BC-BET, an online Bladder Cancer Biomarker Evaluation Tool for evaluating candidate diagnostic and prognostic gene expression biomarkers in bladder cancer. A user simply selects a gene, and BC-BET evaluates the utility of that gene’s expression as a diagnostic and prognostic biomarker. Specifically, BC-BET calculates how strongly a gene’s expression is associated with tumor presence (distinguishing tumor from normal samples), tumor grade (distinguishing low- from high-grade tumors), tumor stage (distinguishing non-muscle invasive from muscle invasive samples), and patient outcome (e.g., disease-specific survival) across all patients in each cohort. Patients with low-grade, non-muscle invasive tumors and patients with high-grade, muscle invasive tumors are also analyzed separately in order to evaluate whether the biomarker of interest has prognostic value independent of grade and stage.ConclusionAlthough bladder cancer gene expression datasets are publicly available, their analysis is computationally intensive and requires bioinformatics expertise. BC-BET is an easy-to-use tool for rapidly evaluating bladder cancer gene expression biomarkers across multiple patient cohorts.Electronic supplementary materialThe online version of this article (doi:10.1186/s12894-015-0056-z) contains supplementary material, which is available to authorized users.
Highlights
In the past ~15 years, the identification of diagnostic and prognostic biomarkers from gene expression data has increased our understanding of cancer biology and has led to advances in the personalized treatment of many cancers
In the past ~15 years, gene expression profiling of human cancers has revolutionized our understanding of cancer as a genetic disease and has expedited the identification of driver mutations and biomarkers for personalized treatment
Examples of prognostic biomarkers in routine clinical use include the OncotypeDx and MammaPrint gene panels, which both predict the likelihood of disease recurrence in breast cancer and provide patients and clinicians with relevant information regarding the potential benefit of chemotherapy [3, 4]
Summary
Bladder cancer gene expression datasets are publicly available, their analysis is computationally intensive and requires bioinformatics expertise.
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